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Atmospheric Effects on OFDM Wireless Links Operating In electronics Article Atmospheric Effects on OFDM Wireless Links Operating in the Millimeter Wave Regime Yosef Golovachev 1,* , Gad A. Pinhasi 2,* and Yosef Pinhasi 1,* 1 Department of Electrical and Electronic Engineering, Ariel University, Ariel 98603, Israel 2 Department of Chemical Engineering, Ariel University, Ariel 98603, Israel * Correspondence: [email protected] (Y.G.); [email protected] (G.A.P.); [email protected] (Y.P.) Received: 6 September 2020; Accepted: 27 September 2020; Published: 29 September 2020 Abstract: The development of millimeter wave communication links and the allocation of bands within the Extremely High Frequency (EHF) range for the next generation cellular network present significant challenges due to the unique propagation effects emerging in this regime of frequencies. This includes susceptibility to amplitude and phase distortions caused by weather conditions. In the current paper, the widely used Orthogonal Division Frequency Multiplexing (OFDM) transmission scheme is tested for resilience against weather-induced attenuation and phase shifts, focusing on the effect of rainfall rates. Operating frequency bands, channel bandwidth, and other modulation parameters were selected according to the 3rd Generation Partnership Project (3GPP) Technical Specification. The performance and the quality of the wireless link is analyzed via constellation diagram and BER (Bit Error Rate) performance chart. Simulation results indicate that OFDM channel performance can be significantly improved by consideration of the local atmospheric conditions while decoding the information by the receiver demodulator. It is also demonstrated that monitoring the weather conditions and employing a corresponding phase compensation assist in the correction of signal distortions caused by the atmospheric dispersion, and consequently leads to a lower bit error rate. Keywords: MMW; OFDM; 5G; 3GPP; atmospheric radio propagation 1. Introduction The rapid growing use of cellular services and the expanding line of products that are based on the wireless communications technologies have led to an increasing interest in the standardization of the 5th Generation (5G) mobile communication technology. The 5G technology is expected to use millimeter waves (MMW) to offer unprecedented spectrum and extremely high data rates to the wireless communications. The implementation of MMW technology has led to the recognition of the necessity of a consistent standardization of networks, channels, and modulation techniques that are relied on corresponding modeling efforts [1]. The US Federal Communications Commission (FCC) has stated their intention to establish the U.S. as leaders in the field of 5G [2]. One of the options considered for application was the allocation of bandwidth in the Extremely High Frequency (EHF) range, 30–300 GHz that can lead to the development of practical consumer applications of MMW technologies. The spectrum available for licensed use was set in three bands within the Ultra High Frequency (UHF) 850 MHz, 2.4 GHz, as well as MMW in the 28 GHz, 37 GHz, and 39 GHz bands. Furthermore, the 64–71 GHz band was recently designated for unlicensed use, which increases the total amount of unlicensed spectrum to 14 GHz as well as 600 MHz in the 37 GHz band for shared access. Different modulation techniques have been proposed for usage in 5G systems in order to provide greater flexibility and thereby increase transmission efficiency and reliability. Currently, the orthogonal Electronics 2020, 9, 1598; doi:10.3390/electronics9101598 www.mdpi.com/journal/electronics Electronics 2020, 9, 1598 2 of 14 frequency-division multiplexing (OFDM) scheme is used in the 4G as a digital multi-carrier modulation method and is one of the popular techniques considered for the 5G cellular wireless communications [3]. The communication in the EHF range has many advantages; however, it is subjected to atmospheric propagation issues. In general, atmospheric effects are due to absorption and dispersion phenomena. In short wavelengths, the MMW propagation is significantly affected by weather conditions, such as air pressure, temperature, relative humidity, fog, and rain. Almost all of the studies on the effect of atmospheric conditions on the performance of communication links are referring to the attenuation only [4]. The signal phase shift and time delay effects are usually ignored, although they can be significant in extreme conditions [5,6]. Pinhasi et al. presented in their previous studies on MMW atmospheric propagation, theoretical and experimental results of absorptive and dispersive propagation effects and their influence on the signal strength and phase dispersion [7–11]. It was found that apart from attenuation, the dispersive effects like group delay should be considered in the design of MMW and THz radar and wireless communication links. In the present work, the performance of an OFDM wireless communication link operating in the MMW regime is studied under different weather conditions such as humidity, fog, and rain. The atmospheric effects on the OFDM waveform were formulated theoretically and demonstrated by simulation model. For this purpose, an atmospheric channel model for wireless communication was developed, combining our MMW propagation model with 5G Toolbox of MATLAB R2019b (MathWorks, Natick, MA, USA) [12]. MMW propagation model is applied to the transmitted baseband OFDM complex envelope and the performance of the wireless link is presented by using constellation diagram and BER (Bit Error Rate) performance chart. The present work is focused on the dispersive effects like group delay and phase shift and their effect on the OFDM communication link. The current paper presents an integration of several issues as sub-models: the MMW propagation model (Section2), the frequency dependent attenuation and phase dispersion factors for di fferent weather conditions (Section3), transmission of OFDM waveform in a medium (Section4), technical specifications of the next generation cellular network and the simulation parameters (Section5). Section6 presents simulations for MMW communication scenarios. The results of the simulation are presented in the form of a constellation diagram and BER performance graph for different atmospheric conditions, focusing on rainfall rate effect (Section7). The novel approach of predicting phase distortions due to the dispersion of the millimeter waves propagating in the atmospheric medium is presented in Section8. It is shown that by employing an adaptive phase compensator, the bit error rate can be considerably reduced. 2. Channel Model Besides modelling of the transmitter and receiver, analyzing the wireless link operating in the millimeter wave regime also requires accurate description of the medium. The transmitted signal ET(t) is propagating in the medium, which can be treated as a linear system via its impulse response h(t). The received signal is the result of the convolution ER(t) = ET(t) h(t). In the presence of Additive ∗ White Gaussian Noise (AWGN) n(t), the signal and noise obtained at the receiver’s input and fed to the demodulator is the summation Ein(t) = ER(t) + n(t). The link is illustrated in a block diagram in Figure1. Figure 1. Block diagram of a wireless link operating in the millimeter wavelengths. Electronics 2020, 9, 1598 3 of 14 The atmospheric medium is characterized as a linear system with a transfer function H(j f ) given in the frequency domain. In the time domain, the transmitted signal ET(t) is presented as a carrier wave at a frequency fc modulated by a wideband complex envelope Aein(t) [8]: 8 9 > > > > > > <> j2π fct=> 1 +j2π fct 1 j2π fct ET(t) = Re>[i(t) + jq(t)] e > = Aein(t) e + Aein∗(t) e− (1) > · > 2 · 2 · >| {z } > > > : Aein(t) ; where Aein(t) = i(t) + jq(t) is a complex envelope, given in terms of the In-phase i(t) and Quadrature q(t) baseband components. A Fourier transformation of Equation (1) is carried out to present the transmitted signal in the frequency domain: + R1 j2π f t ET(j f ) = ET(t) ET(t) e dt = F ≡ · − (2) 1 −∞1 = Ae [j( f fc)] + Ae [ j( f + fc)] 2 in − 2 in∗ − n o where Ae (j f ) = Ae (t) . Using the transfer function H(j f ) of the medium, the received signal can in F in be now expressed in the frequency domain by: ER(j f ) = ET(j f ) H(j f ) = 1 1 · (3) = Ae [j( f fc)] H(j f ) + Ae [ j( f + fc)] H(j f ) 2 in − · 2 in∗ − · Inverse Fourier transformation of Equation (3) results in the signal obtained at the receiver site: + R1 +j2π f t ER(t) = ER(j f )e d f = 8 9 > −∞ > > > > > >8 9 > >>Z+ > > >> 1 > > (4) = Re<< Ae (j f ) H[j( f + f )] e+j2π f td f = ej2π fct= >> in c > > >:> · · ;> · > > > > −∞ > >| {z } > :> ;> Aeout(t) The expression for the received complex envelope is identified in Equation (4) to be: + Z 1 +j2π f t Aeout(t) = Ae (j f ) H[j( f + fc)] e d f (5) in · · −∞ 3. Propagation Factors of the Atmosphere The millimeter wave propagation model (MPM) is used for calculation of the atmospheric frequency response taken into consideration of weather conditions. Contributions of dry air, water vapor, suspended water droplets (haze, fog, cloud), and rain are all addressed in the model. For completeness and clarity of notations, the model [7,9] is hereby reviewed. In general, the propagation of millimeter waves through the atmospheric medium along a distance d is described by the transfer function: q 2π f j n( f ) d H(j f ) = TFS(d)e− c · · (6) Electronics 2020, 9, 1598 4 of 14 where n( f ) is the refractive index of the atmospheric medium and c is the speed of light in vacuum. TFS(d) is the free-space power transmission along a distance d excluding the atmospheric transmittance: !2 c TFS(d) = GR GT (7) 2π fcd where GR and GT are the directivity gains of the receiving and transmitting antennas, respectively. The refractive index n( f ) is a frequency dependent quantity that can be expressed in terms of dispersive complex refractivity N( f ) (given in ppm): 6 n( f ) = 1 + [N0 + N0( f ) jN00 ( f )] 10 (8) | {z− } × N( f ) where the nondispersive part N0 is real and positive constant, N0( f ) and N00 ( f ) are the frequency dependent real and imaginary refractivity terms, respectively.
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